Multi Linear Regression 50 Satartups.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c20896a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import sklearn.linear_model as sk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "13aab178",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>R&amp;D Spend</th>\n",
       "      <th>Administration</th>\n",
       "      <th>Marketing Spend</th>\n",
       "      <th>State</th>\n",
       "      <th>Profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>165349.20</td>\n",
       "      <td>136897.80</td>\n",
       "      <td>471784.10</td>\n",
       "      <td>New York</td>\n",
       "      <td>192261.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>162597.70</td>\n",
       "      <td>151377.59</td>\n",
       "      <td>443898.53</td>\n",
       "      <td>California</td>\n",
       "      <td>191792.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>153441.51</td>\n",
       "      <td>101145.55</td>\n",
       "      <td>407934.54</td>\n",
       "      <td>Florida</td>\n",
       "      <td>191050.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>144372.41</td>\n",
       "      <td>118671.85</td>\n",
       "      <td>383199.62</td>\n",
       "      <td>New York</td>\n",
       "      <td>182901.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>142107.34</td>\n",
       "      <td>91391.77</td>\n",
       "      <td>366168.42</td>\n",
       "      <td>Florida</td>\n",
       "      <td>166187.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>131876.90</td>\n",
       "      <td>99814.71</td>\n",
       "      <td>362861.36</td>\n",
       "      <td>New York</td>\n",
       "      <td>156991.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>134615.46</td>\n",
       "      <td>147198.87</td>\n",
       "      <td>127716.82</td>\n",
       "      <td>California</td>\n",
       "      <td>156122.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>130298.13</td>\n",
       "      <td>145530.06</td>\n",
       "      <td>323876.68</td>\n",
       "      <td>Florida</td>\n",
       "      <td>155752.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>120542.52</td>\n",
       "      <td>148718.95</td>\n",
       "      <td>311613.29</td>\n",
       "      <td>New York</td>\n",
       "      <td>152211.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>123334.88</td>\n",
       "      <td>108679.17</td>\n",
       "      <td>304981.62</td>\n",
       "      <td>California</td>\n",
       "      <td>149759.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>101913.08</td>\n",
       "      <td>110594.11</td>\n",
       "      <td>229160.95</td>\n",
       "      <td>Florida</td>\n",
       "      <td>146121.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>100671.96</td>\n",
       "      <td>91790.61</td>\n",
       "      <td>249744.55</td>\n",
       "      <td>California</td>\n",
       "      <td>144259.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>93863.75</td>\n",
       "      <td>127320.38</td>\n",
       "      <td>249839.44</td>\n",
       "      <td>Florida</td>\n",
       "      <td>141585.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>91992.39</td>\n",
       "      <td>135495.07</td>\n",
       "      <td>252664.93</td>\n",
       "      <td>California</td>\n",
       "      <td>134307.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>119943.24</td>\n",
       "      <td>156547.42</td>\n",
       "      <td>256512.92</td>\n",
       "      <td>Florida</td>\n",
       "      <td>132602.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>114523.61</td>\n",
       "      <td>122616.84</td>\n",
       "      <td>261776.23</td>\n",
       "      <td>New York</td>\n",
       "      <td>129917.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>78013.11</td>\n",
       "      <td>121597.55</td>\n",
       "      <td>264346.06</td>\n",
       "      <td>California</td>\n",
       "      <td>126992.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>94657.16</td>\n",
       "      <td>145077.58</td>\n",
       "      <td>282574.31</td>\n",
       "      <td>New York</td>\n",
       "      <td>125370.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>91749.16</td>\n",
       "      <td>114175.79</td>\n",
       "      <td>294919.57</td>\n",
       "      <td>Florida</td>\n",
       "      <td>124266.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>86419.70</td>\n",
       "      <td>153514.11</td>\n",
       "      <td>0.00</td>\n",
       "      <td>New York</td>\n",
       "      <td>122776.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>76253.86</td>\n",
       "      <td>113867.30</td>\n",
       "      <td>298664.47</td>\n",
       "      <td>California</td>\n",
       "      <td>118474.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>78389.47</td>\n",
       "      <td>153773.43</td>\n",
       "      <td>299737.29</td>\n",
       "      <td>New York</td>\n",
       "      <td>111313.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>73994.56</td>\n",
       "      <td>122782.75</td>\n",
       "      <td>303319.26</td>\n",
       "      <td>Florida</td>\n",
       "      <td>110352.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>67532.53</td>\n",
       "      <td>105751.03</td>\n",
       "      <td>304768.73</td>\n",
       "      <td>Florida</td>\n",
       "      <td>108733.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>77044.01</td>\n",
       "      <td>99281.34</td>\n",
       "      <td>140574.81</td>\n",
       "      <td>New York</td>\n",
       "      <td>108552.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>64664.71</td>\n",
       "      <td>139553.16</td>\n",
       "      <td>137962.62</td>\n",
       "      <td>California</td>\n",
       "      <td>107404.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>75328.87</td>\n",
       "      <td>144135.98</td>\n",
       "      <td>134050.07</td>\n",
       "      <td>Florida</td>\n",
       "      <td>105733.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>72107.60</td>\n",
       "      <td>127864.55</td>\n",
       "      <td>353183.81</td>\n",
       "      <td>New York</td>\n",
       "      <td>105008.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>66051.52</td>\n",
       "      <td>182645.56</td>\n",
       "      <td>118148.20</td>\n",
       "      <td>Florida</td>\n",
       "      <td>103282.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>65605.48</td>\n",
       "      <td>153032.06</td>\n",
       "      <td>107138.38</td>\n",
       "      <td>New York</td>\n",
       "      <td>101004.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>61994.48</td>\n",
       "      <td>115641.28</td>\n",
       "      <td>91131.24</td>\n",
       "      <td>Florida</td>\n",
       "      <td>99937.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>61136.38</td>\n",
       "      <td>152701.92</td>\n",
       "      <td>88218.23</td>\n",
       "      <td>New York</td>\n",
       "      <td>97483.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>63408.86</td>\n",
       "      <td>129219.61</td>\n",
       "      <td>46085.25</td>\n",
       "      <td>California</td>\n",
       "      <td>97427.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>55493.95</td>\n",
       "      <td>103057.49</td>\n",
       "      <td>214634.81</td>\n",
       "      <td>Florida</td>\n",
       "      <td>96778.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>46426.07</td>\n",
       "      <td>157693.92</td>\n",
       "      <td>210797.67</td>\n",
       "      <td>California</td>\n",
       "      <td>96712.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>46014.02</td>\n",
       "      <td>85047.44</td>\n",
       "      <td>205517.64</td>\n",
       "      <td>New York</td>\n",
       "      <td>96479.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>28663.76</td>\n",
       "      <td>127056.21</td>\n",
       "      <td>201126.82</td>\n",
       "      <td>Florida</td>\n",
       "      <td>90708.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>44069.95</td>\n",
       "      <td>51283.14</td>\n",
       "      <td>197029.42</td>\n",
       "      <td>California</td>\n",
       "      <td>89949.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>20229.59</td>\n",
       "      <td>65947.93</td>\n",
       "      <td>185265.10</td>\n",
       "      <td>New York</td>\n",
       "      <td>81229.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>38558.51</td>\n",
       "      <td>82982.09</td>\n",
       "      <td>174999.30</td>\n",
       "      <td>California</td>\n",
       "      <td>81005.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>28754.33</td>\n",
       "      <td>118546.05</td>\n",
       "      <td>172795.67</td>\n",
       "      <td>California</td>\n",
       "      <td>78239.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>27892.92</td>\n",
       "      <td>84710.77</td>\n",
       "      <td>164470.71</td>\n",
       "      <td>Florida</td>\n",
       "      <td>77798.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>23640.93</td>\n",
       "      <td>96189.63</td>\n",
       "      <td>148001.11</td>\n",
       "      <td>California</td>\n",
       "      <td>71498.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>15505.73</td>\n",
       "      <td>127382.30</td>\n",
       "      <td>35534.17</td>\n",
       "      <td>New York</td>\n",
       "      <td>69758.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>22177.74</td>\n",
       "      <td>154806.14</td>\n",
       "      <td>28334.72</td>\n",
       "      <td>California</td>\n",
       "      <td>65200.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>1000.23</td>\n",
       "      <td>124153.04</td>\n",
       "      <td>1903.93</td>\n",
       "      <td>New York</td>\n",
       "      <td>64926.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>1315.46</td>\n",
       "      <td>115816.21</td>\n",
       "      <td>297114.46</td>\n",
       "      <td>Florida</td>\n",
       "      <td>49490.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>0.00</td>\n",
       "      <td>135426.92</td>\n",
       "      <td>0.00</td>\n",
       "      <td>California</td>\n",
       "      <td>42559.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>542.05</td>\n",
       "      <td>51743.15</td>\n",
       "      <td>0.00</td>\n",
       "      <td>New York</td>\n",
       "      <td>35673.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>0.00</td>\n",
       "      <td>116983.80</td>\n",
       "      <td>45173.06</td>\n",
       "      <td>California</td>\n",
       "      <td>14681.40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    R&D Spend  Administration  Marketing Spend       State     Profit\n",
       "0   165349.20       136897.80        471784.10    New York  192261.83\n",
       "1   162597.70       151377.59        443898.53  California  191792.06\n",
       "2   153441.51       101145.55        407934.54     Florida  191050.39\n",
       "3   144372.41       118671.85        383199.62    New York  182901.99\n",
       "4   142107.34        91391.77        366168.42     Florida  166187.94\n",
       "5   131876.90        99814.71        362861.36    New York  156991.12\n",
       "6   134615.46       147198.87        127716.82  California  156122.51\n",
       "7   130298.13       145530.06        323876.68     Florida  155752.60\n",
       "8   120542.52       148718.95        311613.29    New York  152211.77\n",
       "9   123334.88       108679.17        304981.62  California  149759.96\n",
       "10  101913.08       110594.11        229160.95     Florida  146121.95\n",
       "11  100671.96        91790.61        249744.55  California  144259.40\n",
       "12   93863.75       127320.38        249839.44     Florida  141585.52\n",
       "13   91992.39       135495.07        252664.93  California  134307.35\n",
       "14  119943.24       156547.42        256512.92     Florida  132602.65\n",
       "15  114523.61       122616.84        261776.23    New York  129917.04\n",
       "16   78013.11       121597.55        264346.06  California  126992.93\n",
       "17   94657.16       145077.58        282574.31    New York  125370.37\n",
       "18   91749.16       114175.79        294919.57     Florida  124266.90\n",
       "19   86419.70       153514.11             0.00    New York  122776.86\n",
       "20   76253.86       113867.30        298664.47  California  118474.03\n",
       "21   78389.47       153773.43        299737.29    New York  111313.02\n",
       "22   73994.56       122782.75        303319.26     Florida  110352.25\n",
       "23   67532.53       105751.03        304768.73     Florida  108733.99\n",
       "24   77044.01        99281.34        140574.81    New York  108552.04\n",
       "25   64664.71       139553.16        137962.62  California  107404.34\n",
       "26   75328.87       144135.98        134050.07     Florida  105733.54\n",
       "27   72107.60       127864.55        353183.81    New York  105008.31\n",
       "28   66051.52       182645.56        118148.20     Florida  103282.38\n",
       "29   65605.48       153032.06        107138.38    New York  101004.64\n",
       "30   61994.48       115641.28         91131.24     Florida   99937.59\n",
       "31   61136.38       152701.92         88218.23    New York   97483.56\n",
       "32   63408.86       129219.61         46085.25  California   97427.84\n",
       "33   55493.95       103057.49        214634.81     Florida   96778.92\n",
       "34   46426.07       157693.92        210797.67  California   96712.80\n",
       "35   46014.02        85047.44        205517.64    New York   96479.51\n",
       "36   28663.76       127056.21        201126.82     Florida   90708.19\n",
       "37   44069.95        51283.14        197029.42  California   89949.14\n",
       "38   20229.59        65947.93        185265.10    New York   81229.06\n",
       "39   38558.51        82982.09        174999.30  California   81005.76\n",
       "40   28754.33       118546.05        172795.67  California   78239.91\n",
       "41   27892.92        84710.77        164470.71     Florida   77798.83\n",
       "42   23640.93        96189.63        148001.11  California   71498.49\n",
       "43   15505.73       127382.30         35534.17    New York   69758.98\n",
       "44   22177.74       154806.14         28334.72  California   65200.33\n",
       "45    1000.23       124153.04          1903.93    New York   64926.08\n",
       "46    1315.46       115816.21        297114.46     Florida   49490.75\n",
       "47       0.00       135426.92             0.00  California   42559.73\n",
       "48     542.05        51743.15             0.00    New York   35673.41\n",
       "49       0.00       116983.80         45173.06  California   14681.40"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"C:/Users/Sathya/Desktop/Data science assignments/20 November/Multi Linear Regression - Assignment -2/3. Multi Linear Regression/50_Startups.csv\")\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "97b85e3a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df =data.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "793692bf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 50 entries, 0 to 49\n",
      "Data columns (total 5 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   R&D Spend        50 non-null     float64\n",
      " 1   Administration   50 non-null     float64\n",
      " 2   Marketing Spend  50 non-null     float64\n",
      " 3   State            50 non-null     object \n",
      " 4   Profit           50 non-null     float64\n",
      "dtypes: float64(4), object(1)\n",
      "memory usage: 2.1+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b029dc55",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>R&amp;D Spend</th>\n",
       "      <th>Administration</th>\n",
       "      <th>Marketing Spend</th>\n",
       "      <th>Profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>73721.615600</td>\n",
       "      <td>121344.639600</td>\n",
       "      <td>211025.097800</td>\n",
       "      <td>112012.639200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>45902.256482</td>\n",
       "      <td>28017.802755</td>\n",
       "      <td>122290.310726</td>\n",
       "      <td>40306.180338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>51283.140000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14681.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>39936.370000</td>\n",
       "      <td>103730.875000</td>\n",
       "      <td>129300.132500</td>\n",
       "      <td>90138.902500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>73051.080000</td>\n",
       "      <td>122699.795000</td>\n",
       "      <td>212716.240000</td>\n",
       "      <td>107978.190000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>101602.800000</td>\n",
       "      <td>144842.180000</td>\n",
       "      <td>299469.085000</td>\n",
       "      <td>139765.977500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>165349.200000</td>\n",
       "      <td>182645.560000</td>\n",
       "      <td>471784.100000</td>\n",
       "      <td>192261.830000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           R&D Spend  Administration  Marketing Spend         Profit\n",
       "count      50.000000       50.000000        50.000000      50.000000\n",
       "mean    73721.615600   121344.639600    211025.097800  112012.639200\n",
       "std     45902.256482    28017.802755    122290.310726   40306.180338\n",
       "min         0.000000    51283.140000         0.000000   14681.400000\n",
       "25%     39936.370000   103730.875000    129300.132500   90138.902500\n",
       "50%     73051.080000   122699.795000    212716.240000  107978.190000\n",
       "75%    101602.800000   144842.180000    299469.085000  139765.977500\n",
       "max    165349.200000   182645.560000    471784.100000  192261.830000"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "89d4ceac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "R&D Spend          0\n",
       "Administration     0\n",
       "Marketing Spend    0\n",
       "State              0\n",
       "Profit             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "71d13cbe",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "c3616649",
   "metadata": {},
   "source": [
    "With All Parameter (model_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "acc8f871",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['R&D Spend', 'Administration', 'Marketing Spend', 'State', 'Profit'], dtype='object')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "0716a83a",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df[['R&D Spend', 'Administration', 'Marketing Spend']]\n",
    "y = df[['Profit']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "89f01c0c",
   "metadata": {},
   "outputs": [],
   "source": [
    "mlin = sk.LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "083c7001",
   "metadata": {},
   "outputs": [],
   "source": [
    "w = mlin.fit(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "6d913c28",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.80571505, -0.02681597,  0.02722806]])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "616d7afd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([50122.19298987])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "6c52fa04",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9507459940683246"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w.score(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "0263e430",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[192521.25289008],\n",
       "       [189156.76823227],\n",
       "       [182147.2790962 ],\n",
       "       [173696.70002553],\n",
       "       [172139.51418327],\n",
       "       [163580.7805712 ],\n",
       "       [158114.09666865],\n",
       "       [160021.36304781],\n",
       "       [151741.69969865],\n",
       "       [154884.68410995],\n",
       "       [135509.01636714],\n",
       "       [135573.71296074],\n",
       "       [129138.05418243],\n",
       "       [127487.99166275],\n",
       "       [149548.64633453],\n",
       "       [146235.1599852 ],\n",
       "       [116915.40540144],\n",
       "       [130192.44720781],\n",
       "       [129014.2268059 ],\n",
       "       [115635.21636716],\n",
       "       [116639.6692309 ],\n",
       "       [117319.45164029],\n",
       "       [114706.98171695],\n",
       "       [109996.61522126],\n",
       "       [113362.96611314],\n",
       "       [102237.72506481],\n",
       "       [110600.5753503 ],\n",
       "       [114408.07145684],\n",
       "       [101660.02600497],\n",
       "       [101794.98345176],\n",
       "       [ 99452.37293606],\n",
       "       [ 97687.85627575],\n",
       "       [ 99001.32898549],\n",
       "       [ 97915.00780465],\n",
       "       [ 89039.27374116],\n",
       "       [ 90511.59956753],\n",
       "       [ 75286.17458546],\n",
       "       [ 89619.5377079 ],\n",
       "       [ 69697.43064804],\n",
       "       [ 83729.01197692],\n",
       "       [ 74815.95399105],\n",
       "       [ 74802.55623866],\n",
       "       [ 70620.41182056],\n",
       "       [ 60167.03996335],\n",
       "       [ 64611.3549157 ],\n",
       "       [ 47650.64968691],\n",
       "       [ 56166.20685261],\n",
       "       [ 46490.58898335],\n",
       "       [ 49171.38815763],\n",
       "       [ 48215.1341113 ]])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w.predict(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8b42a63d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "3a6eae84",
   "metadata": {},
   "source": [
    "With 'R&D Spend' & 'Administration' (model_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "98149431",
   "metadata": {},
   "outputs": [],
   "source": [
    "x= df[['R&D Spend', 'Administration']]\n",
    "y= df[['Profit']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "98652a4d",
   "metadata": {},
   "outputs": [],
   "source": [
    "mlin1 = sk.LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "174bd55c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v= mlin1.fit(x,y)\n",
    "v"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "a4a83e5d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.86211798, -0.05299543]])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "aceae50f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([54886.62062756])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "d70f3551",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9478129385009173"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v.score(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "143d758a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "aca89aa3",
   "metadata": {},
   "source": [
    "With 'Administration' & 'Marketing Spend' (mode_3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "69b5ed4a",
   "metadata": {},
   "outputs": [],
   "source": [
    "x=df[['Administration', 'Marketing Spend']]\n",
    "y= df[['Profit']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "0c98a4df",
   "metadata": {},
   "outputs": [],
   "source": [
    "mlin2 = sk.LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "29301419",
   "metadata": {},
   "outputs": [],
   "source": [
    "t = mlin2.fit(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "5152530e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.32367271, 0.24884368]])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "602be3f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([20224.42905876])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "f7dd3d68",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6097229746770664"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t.score(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d742e8d1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "45e71307",
   "metadata": {},
   "source": [
    "With 'R&D Spend' & 'Marketing Spend' (model_4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "9c6ba8da",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "x = df[['R&D Spend','Marketing Spend']]\n",
    "y = df[['Profit']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "94100965",
   "metadata": {},
   "outputs": [],
   "source": [
    "mlin3= sk.LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "e45f445f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = mlin3.fit(x,y)\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "e3ad4f9e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.79658404, 0.02990788]])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "192bba02",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([46975.86422072])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "b05531f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9504503015559763"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.score(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f4fafbd9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "1c5234fe",
   "metadata": {},
   "source": [
    "Above all of the model, The best model is model_1 which is 'With All Parameter' because the score value is near to 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "dec95bf5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter the value of R&D Spend 165349.20\n",
      "Enter the value of Administration 136897.80\n",
      "Enter the value of Marketing Spend 47184.10\n",
      "The predicted profit is  [180960.21657565]\n"
     ]
    }
   ],
   "source": [
    "x1 = float(input('Enter the value of R&D Spend '))\n",
    "x2 = float(input('Enter the value of Administration '))\n",
    "x3 = float(input('Enter the value of Marketing Spend '))\n",
    "pred_profit = w.coef_[0][0]*x1 + w.coef_[0][1]*x2 + w.coef_[0][2]*x3 + w.intercept_\n",
    "print('The predicted profit is ',pred_profit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "40554c59",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['pred_profit'] = w.predict(x) # firtst run x any y which is in model_1 then run this cell"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "0f1adce2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>R&amp;D Spend</th>\n",
       "      <th>Administration</th>\n",
       "      <th>Marketing Spend</th>\n",
       "      <th>State</th>\n",
       "      <th>Profit</th>\n",
       "      <th>pred_profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>165349.20</td>\n",
       "      <td>136897.80</td>\n",
       "      <td>471784.10</td>\n",
       "      <td>New York</td>\n",
       "      <td>192261.83</td>\n",
       "      <td>192521.252890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>162597.70</td>\n",
       "      <td>151377.59</td>\n",
       "      <td>443898.53</td>\n",
       "      <td>California</td>\n",
       "      <td>191792.06</td>\n",
       "      <td>189156.768232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>153441.51</td>\n",
       "      <td>101145.55</td>\n",
       "      <td>407934.54</td>\n",
       "      <td>Florida</td>\n",
       "      <td>191050.39</td>\n",
       "      <td>182147.279096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>144372.41</td>\n",
       "      <td>118671.85</td>\n",
       "      <td>383199.62</td>\n",
       "      <td>New York</td>\n",
       "      <td>182901.99</td>\n",
       "      <td>173696.700026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>142107.34</td>\n",
       "      <td>91391.77</td>\n",
       "      <td>366168.42</td>\n",
       "      <td>Florida</td>\n",
       "      <td>166187.94</td>\n",
       "      <td>172139.514183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>131876.90</td>\n",
       "      <td>99814.71</td>\n",
       "      <td>362861.36</td>\n",
       "      <td>New York</td>\n",
       "      <td>156991.12</td>\n",
       "      <td>163580.780571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>134615.46</td>\n",
       "      <td>147198.87</td>\n",
       "      <td>127716.82</td>\n",
       "      <td>California</td>\n",
       "      <td>156122.51</td>\n",
       "      <td>158114.096669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>130298.13</td>\n",
       "      <td>145530.06</td>\n",
       "      <td>323876.68</td>\n",
       "      <td>Florida</td>\n",
       "      <td>155752.60</td>\n",
       "      <td>160021.363048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>120542.52</td>\n",
       "      <td>148718.95</td>\n",
       "      <td>311613.29</td>\n",
       "      <td>New York</td>\n",
       "      <td>152211.77</td>\n",
       "      <td>151741.699699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>123334.88</td>\n",
       "      <td>108679.17</td>\n",
       "      <td>304981.62</td>\n",
       "      <td>California</td>\n",
       "      <td>149759.96</td>\n",
       "      <td>154884.684110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>101913.08</td>\n",
       "      <td>110594.11</td>\n",
       "      <td>229160.95</td>\n",
       "      <td>Florida</td>\n",
       "      <td>146121.95</td>\n",
       "      <td>135509.016367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>100671.96</td>\n",
       "      <td>91790.61</td>\n",
       "      <td>249744.55</td>\n",
       "      <td>California</td>\n",
       "      <td>144259.40</td>\n",
       "      <td>135573.712961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>93863.75</td>\n",
       "      <td>127320.38</td>\n",
       "      <td>249839.44</td>\n",
       "      <td>Florida</td>\n",
       "      <td>141585.52</td>\n",
       "      <td>129138.054182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>91992.39</td>\n",
       "      <td>135495.07</td>\n",
       "      <td>252664.93</td>\n",
       "      <td>California</td>\n",
       "      <td>134307.35</td>\n",
       "      <td>127487.991663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>119943.24</td>\n",
       "      <td>156547.42</td>\n",
       "      <td>256512.92</td>\n",
       "      <td>Florida</td>\n",
       "      <td>132602.65</td>\n",
       "      <td>149548.646335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>114523.61</td>\n",
       "      <td>122616.84</td>\n",
       "      <td>261776.23</td>\n",
       "      <td>New York</td>\n",
       "      <td>129917.04</td>\n",
       "      <td>146235.159985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>78013.11</td>\n",
       "      <td>121597.55</td>\n",
       "      <td>264346.06</td>\n",
       "      <td>California</td>\n",
       "      <td>126992.93</td>\n",
       "      <td>116915.405401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>94657.16</td>\n",
       "      <td>145077.58</td>\n",
       "      <td>282574.31</td>\n",
       "      <td>New York</td>\n",
       "      <td>125370.37</td>\n",
       "      <td>130192.447208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>91749.16</td>\n",
       "      <td>114175.79</td>\n",
       "      <td>294919.57</td>\n",
       "      <td>Florida</td>\n",
       "      <td>124266.90</td>\n",
       "      <td>129014.226806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>86419.70</td>\n",
       "      <td>153514.11</td>\n",
       "      <td>0.00</td>\n",
       "      <td>New York</td>\n",
       "      <td>122776.86</td>\n",
       "      <td>115635.216367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>76253.86</td>\n",
       "      <td>113867.30</td>\n",
       "      <td>298664.47</td>\n",
       "      <td>California</td>\n",
       "      <td>118474.03</td>\n",
       "      <td>116639.669231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>78389.47</td>\n",
       "      <td>153773.43</td>\n",
       "      <td>299737.29</td>\n",
       "      <td>New York</td>\n",
       "      <td>111313.02</td>\n",
       "      <td>117319.451640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>73994.56</td>\n",
       "      <td>122782.75</td>\n",
       "      <td>303319.26</td>\n",
       "      <td>Florida</td>\n",
       "      <td>110352.25</td>\n",
       "      <td>114706.981717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>67532.53</td>\n",
       "      <td>105751.03</td>\n",
       "      <td>304768.73</td>\n",
       "      <td>Florida</td>\n",
       "      <td>108733.99</td>\n",
       "      <td>109996.615221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>77044.01</td>\n",
       "      <td>99281.34</td>\n",
       "      <td>140574.81</td>\n",
       "      <td>New York</td>\n",
       "      <td>108552.04</td>\n",
       "      <td>113362.966113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>64664.71</td>\n",
       "      <td>139553.16</td>\n",
       "      <td>137962.62</td>\n",
       "      <td>California</td>\n",
       "      <td>107404.34</td>\n",
       "      <td>102237.725065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>75328.87</td>\n",
       "      <td>144135.98</td>\n",
       "      <td>134050.07</td>\n",
       "      <td>Florida</td>\n",
       "      <td>105733.54</td>\n",
       "      <td>110600.575350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>72107.60</td>\n",
       "      <td>127864.55</td>\n",
       "      <td>353183.81</td>\n",
       "      <td>New York</td>\n",
       "      <td>105008.31</td>\n",
       "      <td>114408.071457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>66051.52</td>\n",
       "      <td>182645.56</td>\n",
       "      <td>118148.20</td>\n",
       "      <td>Florida</td>\n",
       "      <td>103282.38</td>\n",
       "      <td>101660.026005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>65605.48</td>\n",
       "      <td>153032.06</td>\n",
       "      <td>107138.38</td>\n",
       "      <td>New York</td>\n",
       "      <td>101004.64</td>\n",
       "      <td>101794.983452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>61994.48</td>\n",
       "      <td>115641.28</td>\n",
       "      <td>91131.24</td>\n",
       "      <td>Florida</td>\n",
       "      <td>99937.59</td>\n",
       "      <td>99452.372936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>61136.38</td>\n",
       "      <td>152701.92</td>\n",
       "      <td>88218.23</td>\n",
       "      <td>New York</td>\n",
       "      <td>97483.56</td>\n",
       "      <td>97687.856276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>63408.86</td>\n",
       "      <td>129219.61</td>\n",
       "      <td>46085.25</td>\n",
       "      <td>California</td>\n",
       "      <td>97427.84</td>\n",
       "      <td>99001.328985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>55493.95</td>\n",
       "      <td>103057.49</td>\n",
       "      <td>214634.81</td>\n",
       "      <td>Florida</td>\n",
       "      <td>96778.92</td>\n",
       "      <td>97915.007805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>46426.07</td>\n",
       "      <td>157693.92</td>\n",
       "      <td>210797.67</td>\n",
       "      <td>California</td>\n",
       "      <td>96712.80</td>\n",
       "      <td>89039.273741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>46014.02</td>\n",
       "      <td>85047.44</td>\n",
       "      <td>205517.64</td>\n",
       "      <td>New York</td>\n",
       "      <td>96479.51</td>\n",
       "      <td>90511.599568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>28663.76</td>\n",
       "      <td>127056.21</td>\n",
       "      <td>201126.82</td>\n",
       "      <td>Florida</td>\n",
       "      <td>90708.19</td>\n",
       "      <td>75286.174585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>44069.95</td>\n",
       "      <td>51283.14</td>\n",
       "      <td>197029.42</td>\n",
       "      <td>California</td>\n",
       "      <td>89949.14</td>\n",
       "      <td>89619.537708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>20229.59</td>\n",
       "      <td>65947.93</td>\n",
       "      <td>185265.10</td>\n",
       "      <td>New York</td>\n",
       "      <td>81229.06</td>\n",
       "      <td>69697.430648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>38558.51</td>\n",
       "      <td>82982.09</td>\n",
       "      <td>174999.30</td>\n",
       "      <td>California</td>\n",
       "      <td>81005.76</td>\n",
       "      <td>83729.011977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>28754.33</td>\n",
       "      <td>118546.05</td>\n",
       "      <td>172795.67</td>\n",
       "      <td>California</td>\n",
       "      <td>78239.91</td>\n",
       "      <td>74815.953991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>27892.92</td>\n",
       "      <td>84710.77</td>\n",
       "      <td>164470.71</td>\n",
       "      <td>Florida</td>\n",
       "      <td>77798.83</td>\n",
       "      <td>74802.556239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>23640.93</td>\n",
       "      <td>96189.63</td>\n",
       "      <td>148001.11</td>\n",
       "      <td>California</td>\n",
       "      <td>71498.49</td>\n",
       "      <td>70620.411821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>15505.73</td>\n",
       "      <td>127382.30</td>\n",
       "      <td>35534.17</td>\n",
       "      <td>New York</td>\n",
       "      <td>69758.98</td>\n",
       "      <td>60167.039963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>22177.74</td>\n",
       "      <td>154806.14</td>\n",
       "      <td>28334.72</td>\n",
       "      <td>California</td>\n",
       "      <td>65200.33</td>\n",
       "      <td>64611.354916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>1000.23</td>\n",
       "      <td>124153.04</td>\n",
       "      <td>1903.93</td>\n",
       "      <td>New York</td>\n",
       "      <td>64926.08</td>\n",
       "      <td>47650.649687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>1315.46</td>\n",
       "      <td>115816.21</td>\n",
       "      <td>297114.46</td>\n",
       "      <td>Florida</td>\n",
       "      <td>49490.75</td>\n",
       "      <td>56166.206853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>0.00</td>\n",
       "      <td>135426.92</td>\n",
       "      <td>0.00</td>\n",
       "      <td>California</td>\n",
       "      <td>42559.73</td>\n",
       "      <td>46490.588983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>542.05</td>\n",
       "      <td>51743.15</td>\n",
       "      <td>0.00</td>\n",
       "      <td>New York</td>\n",
       "      <td>35673.41</td>\n",
       "      <td>49171.388158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>0.00</td>\n",
       "      <td>116983.80</td>\n",
       "      <td>45173.06</td>\n",
       "      <td>California</td>\n",
       "      <td>14681.40</td>\n",
       "      <td>48215.134111</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    R&D Spend  Administration  Marketing Spend       State     Profit  \\\n",
       "0   165349.20       136897.80        471784.10    New York  192261.83   \n",
       "1   162597.70       151377.59        443898.53  California  191792.06   \n",
       "2   153441.51       101145.55        407934.54     Florida  191050.39   \n",
       "3   144372.41       118671.85        383199.62    New York  182901.99   \n",
       "4   142107.34        91391.77        366168.42     Florida  166187.94   \n",
       "5   131876.90        99814.71        362861.36    New York  156991.12   \n",
       "6   134615.46       147198.87        127716.82  California  156122.51   \n",
       "7   130298.13       145530.06        323876.68     Florida  155752.60   \n",
       "8   120542.52       148718.95        311613.29    New York  152211.77   \n",
       "9   123334.88       108679.17        304981.62  California  149759.96   \n",
       "10  101913.08       110594.11        229160.95     Florida  146121.95   \n",
       "11  100671.96        91790.61        249744.55  California  144259.40   \n",
       "12   93863.75       127320.38        249839.44     Florida  141585.52   \n",
       "13   91992.39       135495.07        252664.93  California  134307.35   \n",
       "14  119943.24       156547.42        256512.92     Florida  132602.65   \n",
       "15  114523.61       122616.84        261776.23    New York  129917.04   \n",
       "16   78013.11       121597.55        264346.06  California  126992.93   \n",
       "17   94657.16       145077.58        282574.31    New York  125370.37   \n",
       "18   91749.16       114175.79        294919.57     Florida  124266.90   \n",
       "19   86419.70       153514.11             0.00    New York  122776.86   \n",
       "20   76253.86       113867.30        298664.47  California  118474.03   \n",
       "21   78389.47       153773.43        299737.29    New York  111313.02   \n",
       "22   73994.56       122782.75        303319.26     Florida  110352.25   \n",
       "23   67532.53       105751.03        304768.73     Florida  108733.99   \n",
       "24   77044.01        99281.34        140574.81    New York  108552.04   \n",
       "25   64664.71       139553.16        137962.62  California  107404.34   \n",
       "26   75328.87       144135.98        134050.07     Florida  105733.54   \n",
       "27   72107.60       127864.55        353183.81    New York  105008.31   \n",
       "28   66051.52       182645.56        118148.20     Florida  103282.38   \n",
       "29   65605.48       153032.06        107138.38    New York  101004.64   \n",
       "30   61994.48       115641.28         91131.24     Florida   99937.59   \n",
       "31   61136.38       152701.92         88218.23    New York   97483.56   \n",
       "32   63408.86       129219.61         46085.25  California   97427.84   \n",
       "33   55493.95       103057.49        214634.81     Florida   96778.92   \n",
       "34   46426.07       157693.92        210797.67  California   96712.80   \n",
       "35   46014.02        85047.44        205517.64    New York   96479.51   \n",
       "36   28663.76       127056.21        201126.82     Florida   90708.19   \n",
       "37   44069.95        51283.14        197029.42  California   89949.14   \n",
       "38   20229.59        65947.93        185265.10    New York   81229.06   \n",
       "39   38558.51        82982.09        174999.30  California   81005.76   \n",
       "40   28754.33       118546.05        172795.67  California   78239.91   \n",
       "41   27892.92        84710.77        164470.71     Florida   77798.83   \n",
       "42   23640.93        96189.63        148001.11  California   71498.49   \n",
       "43   15505.73       127382.30         35534.17    New York   69758.98   \n",
       "44   22177.74       154806.14         28334.72  California   65200.33   \n",
       "45    1000.23       124153.04          1903.93    New York   64926.08   \n",
       "46    1315.46       115816.21        297114.46     Florida   49490.75   \n",
       "47       0.00       135426.92             0.00  California   42559.73   \n",
       "48     542.05        51743.15             0.00    New York   35673.41   \n",
       "49       0.00       116983.80         45173.06  California   14681.40   \n",
       "\n",
       "      pred_profit  \n",
       "0   192521.252890  \n",
       "1   189156.768232  \n",
       "2   182147.279096  \n",
       "3   173696.700026  \n",
       "4   172139.514183  \n",
       "5   163580.780571  \n",
       "6   158114.096669  \n",
       "7   160021.363048  \n",
       "8   151741.699699  \n",
       "9   154884.684110  \n",
       "10  135509.016367  \n",
       "11  135573.712961  \n",
       "12  129138.054182  \n",
       "13  127487.991663  \n",
       "14  149548.646335  \n",
       "15  146235.159985  \n",
       "16  116915.405401  \n",
       "17  130192.447208  \n",
       "18  129014.226806  \n",
       "19  115635.216367  \n",
       "20  116639.669231  \n",
       "21  117319.451640  \n",
       "22  114706.981717  \n",
       "23  109996.615221  \n",
       "24  113362.966113  \n",
       "25  102237.725065  \n",
       "26  110600.575350  \n",
       "27  114408.071457  \n",
       "28  101660.026005  \n",
       "29  101794.983452  \n",
       "30   99452.372936  \n",
       "31   97687.856276  \n",
       "32   99001.328985  \n",
       "33   97915.007805  \n",
       "34   89039.273741  \n",
       "35   90511.599568  \n",
       "36   75286.174585  \n",
       "37   89619.537708  \n",
       "38   69697.430648  \n",
       "39   83729.011977  \n",
       "40   74815.953991  \n",
       "41   74802.556239  \n",
       "42   70620.411821  \n",
       "43   60167.039963  \n",
       "44   64611.354916  \n",
       "45   47650.649687  \n",
       "46   56166.206853  \n",
       "47   46490.588983  \n",
       "48   49171.388158  \n",
       "49   48215.134111  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19c8c001",
   "metadata": {},
   "source": [
    "State wise model with considering all parameter\n",
    "\n",
    "\n",
    "New York"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "47f3fcc7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['New York', 'California', 'Florida'], dtype=object)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.State.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "6e38a0a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "dfn = df[df['State']=='New York']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "5b98a83f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>R&amp;D Spend</th>\n",
       "      <th>Administration</th>\n",
       "      <th>Marketing Spend</th>\n",
       "      <th>State</th>\n",
       "      <th>Profit</th>\n",
       "      <th>pred_profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>165349.20</td>\n",
       "      <td>136897.80</td>\n",
       "      <td>471784.10</td>\n",
       "      <td>New York</td>\n",
       "      <td>192261.83</td>\n",
       "      <td>192521.252890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>144372.41</td>\n",
       "      <td>118671.85</td>\n",
       "      <td>383199.62</td>\n",
       "      <td>New York</td>\n",
       "      <td>182901.99</td>\n",
       "      <td>173696.700026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>131876.90</td>\n",
       "      <td>99814.71</td>\n",
       "      <td>362861.36</td>\n",
       "      <td>New York</td>\n",
       "      <td>156991.12</td>\n",
       "      <td>163580.780571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>120542.52</td>\n",
       "      <td>148718.95</td>\n",
       "      <td>311613.29</td>\n",
       "      <td>New York</td>\n",
       "      <td>152211.77</td>\n",
       "      <td>151741.699699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>114523.61</td>\n",
       "      <td>122616.84</td>\n",
       "      <td>261776.23</td>\n",
       "      <td>New York</td>\n",
       "      <td>129917.04</td>\n",
       "      <td>146235.159985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>94657.16</td>\n",
       "      <td>145077.58</td>\n",
       "      <td>282574.31</td>\n",
       "      <td>New York</td>\n",
       "      <td>125370.37</td>\n",
       "      <td>130192.447208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>86419.70</td>\n",
       "      <td>153514.11</td>\n",
       "      <td>0.00</td>\n",
       "      <td>New York</td>\n",
       "      <td>122776.86</td>\n",
       "      <td>115635.216367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>78389.47</td>\n",
       "      <td>153773.43</td>\n",
       "      <td>299737.29</td>\n",
       "      <td>New York</td>\n",
       "      <td>111313.02</td>\n",
       "      <td>117319.451640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>77044.01</td>\n",
       "      <td>99281.34</td>\n",
       "      <td>140574.81</td>\n",
       "      <td>New York</td>\n",
       "      <td>108552.04</td>\n",
       "      <td>113362.966113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>72107.60</td>\n",
       "      <td>127864.55</td>\n",
       "      <td>353183.81</td>\n",
       "      <td>New York</td>\n",
       "      <td>105008.31</td>\n",
       "      <td>114408.071457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>65605.48</td>\n",
       "      <td>153032.06</td>\n",
       "      <td>107138.38</td>\n",
       "      <td>New York</td>\n",
       "      <td>101004.64</td>\n",
       "      <td>101794.983452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>61136.38</td>\n",
       "      <td>152701.92</td>\n",
       "      <td>88218.23</td>\n",
       "      <td>New York</td>\n",
       "      <td>97483.56</td>\n",
       "      <td>97687.856276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>46014.02</td>\n",
       "      <td>85047.44</td>\n",
       "      <td>205517.64</td>\n",
       "      <td>New York</td>\n",
       "      <td>96479.51</td>\n",
       "      <td>90511.599568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>20229.59</td>\n",
       "      <td>65947.93</td>\n",
       "      <td>185265.10</td>\n",
       "      <td>New York</td>\n",
       "      <td>81229.06</td>\n",
       "      <td>69697.430648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>15505.73</td>\n",
       "      <td>127382.30</td>\n",
       "      <td>35534.17</td>\n",
       "      <td>New York</td>\n",
       "      <td>69758.98</td>\n",
       "      <td>60167.039963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>1000.23</td>\n",
       "      <td>124153.04</td>\n",
       "      <td>1903.93</td>\n",
       "      <td>New York</td>\n",
       "      <td>64926.08</td>\n",
       "      <td>47650.649687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>542.05</td>\n",
       "      <td>51743.15</td>\n",
       "      <td>0.00</td>\n",
       "      <td>New York</td>\n",
       "      <td>35673.41</td>\n",
       "      <td>49171.388158</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    R&D Spend  Administration  Marketing Spend     State     Profit  \\\n",
       "0   165349.20       136897.80        471784.10  New York  192261.83   \n",
       "3   144372.41       118671.85        383199.62  New York  182901.99   \n",
       "5   131876.90        99814.71        362861.36  New York  156991.12   \n",
       "8   120542.52       148718.95        311613.29  New York  152211.77   \n",
       "15  114523.61       122616.84        261776.23  New York  129917.04   \n",
       "17   94657.16       145077.58        282574.31  New York  125370.37   \n",
       "19   86419.70       153514.11             0.00  New York  122776.86   \n",
       "21   78389.47       153773.43        299737.29  New York  111313.02   \n",
       "24   77044.01        99281.34        140574.81  New York  108552.04   \n",
       "27   72107.60       127864.55        353183.81  New York  105008.31   \n",
       "29   65605.48       153032.06        107138.38  New York  101004.64   \n",
       "31   61136.38       152701.92         88218.23  New York   97483.56   \n",
       "35   46014.02        85047.44        205517.64  New York   96479.51   \n",
       "38   20229.59        65947.93        185265.10  New York   81229.06   \n",
       "43   15505.73       127382.30         35534.17  New York   69758.98   \n",
       "45    1000.23       124153.04          1903.93  New York   64926.08   \n",
       "48     542.05        51743.15             0.00  New York   35673.41   \n",
       "\n",
       "      pred_profit  \n",
       "0   192521.252890  \n",
       "3   173696.700026  \n",
       "5   163580.780571  \n",
       "8   151741.699699  \n",
       "15  146235.159985  \n",
       "17  130192.447208  \n",
       "19  115635.216367  \n",
       "21  117319.451640  \n",
       "24  113362.966113  \n",
       "27  114408.071457  \n",
       "29  101794.983452  \n",
       "31   97687.856276  \n",
       "35   90511.599568  \n",
       "38   69697.430648  \n",
       "43   60167.039963  \n",
       "45   47650.649687  \n",
       "48   49171.388158  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "7f37940b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['R&D Spend', 'Administration', 'Marketing Spend', 'State', 'Profit',\n",
       "       'pred_profit'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "dfn.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "40b6702e",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = dfn[['R&D Spend', 'Administration', 'Marketing Spend']]\n",
    "y = dfn[['Profit']]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "d498880e",
   "metadata": {},
   "outputs": [],
   "source": [
    "n = sk.LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "046e9feb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n.fit(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "1098c429",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.75206935, 0.02522624, 0.02231501]])"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "f7fd943e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([48804.09134713])"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "f3acaaac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9554635470916312"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n.score(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2616694e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "211b6c3e",
   "metadata": {},
   "source": [
    "California"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "fab7537b",
   "metadata": {},
   "outputs": [],
   "source": [
    "dfc = df[df['State']=='California']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "532947e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = dfc[['R&D Spend', 'Administration', 'Marketing Spend']]\n",
    "y = dfc[['Profit']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "8f661e19",
   "metadata": {},
   "outputs": [],
   "source": [
    "c = sk.LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "066bc66b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c.fit(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "5310ba97",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.81178612, 0.01831775, 0.06209629]])"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "e1f8815e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([37870.0547163])"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "db281490",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9617451377483132"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c.score(x,y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19882a9e",
   "metadata": {},
   "source": [
    "Florida"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "3e58a7c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "dff = df[df['State']=='Florida']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "014a2509",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = dff[['R&D Spend', 'Administration', 'Marketing Spend']]\n",
    "y = dff[['Profit']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "9ca192d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "f = sk.LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "f10a686f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f.fit(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "6bf60391",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.81385258, -0.12777446,  0.00309665]])"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "2c75cb7c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([67869.65758237])"
      ]
     },
     "execution_count": 73,
     "metadata": {},
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  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "75edaeaf",
   "metadata": {},
   "outputs": [
    {
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      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
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   "cell_type": "code",
   "execution_count": null,
   "id": "c91fbc38",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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